(小5号黑正)：As the demand for intelligent application in airborne embedded computing system is increasing, the convolution neural network model with excellent performance can effectively solve the problems of target recognition and edge detection in air combat scenes. However, the huge weight parameters and complex network layer structure of convolutional neural network make its computational complexity too high, and the required computing resources and storage resources also increase rapidly with the increase of network layers, so it is difficult to deploy in airborne embedded computing systems with strict requirements on resources and power consumption, which restricts the development of airborne embedded computing systems towards high intelligence. Aiming at the demand of ultra-lightweight intelligent computing in the resource-constrained airborne embedded computing system, a set of optimization and acceleration strategy of convolutional neural network model is proposed. After ultra-lightweight processing of the algorithm model, a convolutional neural network accelerator is built by combining acceleration operators, and the function verification of network model reasoning process is carried out based on FPGA. The experimental results show that the designed accelerator can significantly reduce the occupancy rate of hardware resources and obtain a good algorithm speedup ratio, which has important technical significance for the design of airborne embedded intelligent computing system.